The traditional quadratic programming approach to port folio optimisation is difficult to implement when there are cardinality constraints. Recent approaches to resolving this have used heuristic algorithms to search for points on the cardinality constrained frontier. However, these can be computationally expensive when the practitioner does not know α priori exactly how many assets they may desire in a portfolio, or what level of return/risk they wish to be exposed to without recourse to analysing the actual trade-off frontier. This, study introduces a parallel solution to this problem. By extending techniques developed in the multi-objective evolutionary optimisation domain, a set of portfolios representing estimates of all possible cardinality constrained frontiers can be found in a single search process, for a range of portfolio sizes and constraints. Empirical results are provided on emerging markets and US asset data, and compared to unconstrained frontiers found by quadratic programming. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Fieldsend, J. E., Matatko, J., & Peng, M. (2004). Cardinality constrained portfolio optimisation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 788–793. https://doi.org/10.1007/978-3-540-28651-6_117
Mendeley helps you to discover research relevant for your work.